In general, a dramatic improvement in the survival rate of the cancer can be expected by early detection and treatment. In recent years, mammographic X-ray radiography (mammography) is often used as effective means for early detection of breast cancer. Mammography can examine the state of the entire breast and can effectively detect very small lesions which are hard to detect by visual and palpation-based examinations and in ultrasound images.
However, when the number of patients examined by mammography increases, the physician's load increases, which can cause misdiagnosis or overlooking of lesions due to fatigue or the like. Thus, in order to reduce the medical examiner's load and improve the diagnosis accuracy, a computer-aided detection or diagnosis (CAD) system has been developed. CAD is a technique for improving the quality and productivity of diagnosis such as an improvement in diagnosis accuracy and a reduction in the time required for diagnosis by presenting computer-aided image analysis information to a radiologist as a second opinion.
Examples of major imaging findings on breast cancer include microcalcifications, masses, and architectural distortions. Microcalcifications are the dead and deposited cells of blood vessels or soft tissues in the breast and visually recognized as clusters of white specks on an image. Masses are space-occupying lesions having features in their shape, boundary, and density and appear as shadows having a certain extent of area on an image. Architectural distortions are not clear mass shadows but are lesions in which a normal mammary gland architecture involves distortions and have features such as speculations wherein the mammary gland scatters in a radial form from one point and substantial local retractions or distortions of the mammary gland. This architectural distortion is a lesion that is more difficult to interpret and more likely to be overlooked than other findings.
Since these findings on breast cancer have respective specific image features, CAD systems that detect respective specific findings have been developed (for example, see Patent Literature 1). Among these findings, as for architectural distortions that are particularly difficult to interpret, CAD systems based on various approaches have been proposed. For example, a method of calculating a linear concentration and a directional distribution index of a linear component of an image texture of a region to calculate candidates for architectural distortions based on the product of the two values is known (for example, see Patent Literature 2). Moreover, a method of enhancing a linear component of an original image texture of a region by the Radon transform and enhancing a radial linear component associated with the shadows of architectural distortions using a radial filter designed exclusively for the radial linear component to detect the candidates for architectural distortions is known. Further, a method of creating a model by diagnosing an orientation map obtained by Gabor filters as a vector field diagram of a linear dynamic system and quantifying the degree of radial linear components of an image texture of a region of a lesion based on the model to detect the candidates for architectural distortions (for example, see Non-Patent Literature 2).
These conventional CAD systems for detecting architectural distortions employ an approach that focuses on features unique to architectural distortions such as scattering (speculations) of linear components of an image texture of a region to quantify the feature. However, X-ray image of the breasts provide extremely high accuracy in terms of spatial resolution and density re whereas the images are likely to contain noise and linear components associated with lesions often appear as shadows that are dimly visible and have extremely low contrast. Thus, it is difficult to extract linear components with sufficient accuracy for determining whether the linear components are lesions and a true-positive fraction is not high enough. Moreover, even when the linear components of the mammary gland are extracted, it is difficult to accurately quantify the feature of a lesion and a false-positive fraction is high.
Thus, the present inventors have proposed a method of detecting the candidates for architectural distortions by focusing on an average intensity difference between a target area and the surrounding area rather than extracting fine linear components of an image texture of a region of which the extraction accuracy is poor (for example, see Non-Patent Literature 3). This method focuses on a new feature that a contrast is present between the central portion of a lesion and its surrounding portion to detect lesion candidates having the feature by DoG (Difference of Gaussians) filtering. It is confirmed that this method can detect architectural distortion candidates with a higher true-positive fraction and a lower false-positive fraction at the same true-positive fraction than the conventional CAD systems.
A method of improving the contrast of images associated with lesions in which the linear components of an image texture of a region have an extremely low contrast has been proposed (for example, see Patent Literature 3). However, this method is used for facilitating the interpretation by physicians but is not used for CAD systems. Moreover, a CAD system which uses ultrasound or MRI images other than mammography has been developed (for example, see Patent Literature 4). However, since the images have a lower resolution than mammography, such a CAD system is rarely used. Further, the present inventors has developed a CAD system that extracts inclination segment information from the shadows in an X-ray or CT medical tomographic image using Gabor filters in order to diagnose lung cancer or the like, calculates the feature quantities of the shadows from the information, and determine whether the shadows are abnormal shadows (for example, see Patent Literature 5).